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Dataset Open Access

EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation

Hung, Hsiao-Tzu; Ching, Joann; Doh, Seungheon; Kim, Nabin; Nam, Juhan; Yang, Yi-Hsuan


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  <identifier identifierType="DOI">10.5281/zenodo.5090631</identifier>
  <creators>
    <creator>
      <creatorName>Hung, Hsiao-Tzu</creatorName>
      <givenName>Hsiao-Tzu</givenName>
      <familyName>Hung</familyName>
      <affiliation>Academia Sinica</affiliation>
    </creator>
    <creator>
      <creatorName>Ching, Joann</creatorName>
      <givenName>Joann</givenName>
      <familyName>Ching</familyName>
      <affiliation>Academia Sinica</affiliation>
    </creator>
    <creator>
      <creatorName>Doh, Seungheon</creatorName>
      <givenName>Seungheon</givenName>
      <familyName>Doh</familyName>
      <affiliation>KAIST</affiliation>
    </creator>
    <creator>
      <creatorName>Kim, Nabin</creatorName>
      <givenName>Nabin</givenName>
      <familyName>Kim</familyName>
      <affiliation>Georgia Institute of Technology</affiliation>
    </creator>
    <creator>
      <creatorName>Nam, Juhan</creatorName>
      <givenName>Juhan</givenName>
      <familyName>Nam</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-2664-2119</nameIdentifier>
      <affiliation>KAIST</affiliation>
    </creator>
    <creator>
      <creatorName>Yang, Yi-Hsuan</creatorName>
      <givenName>Yi-Hsuan</givenName>
      <familyName>Yang</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-2724-6161</nameIdentifier>
      <affiliation>Academia Sinica</affiliation>
    </creator>
  </creators>
  <titles>
    <title>EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>piano</subject>
    <subject>emotion</subject>
    <subject>music</subject>
    <subject>midi</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-07-18</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5090631</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.5090630</relatedIdentifier>
  </relatedIdentifiers>
  <version>1.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;EMOPIA (pronounced &amp;lsquo;yee-m&amp;ograve;-pi-uh&amp;rsquo;) dataset is a shared multi-modal (audio and MIDI) database focusing on perceived emotion in&amp;nbsp;&lt;strong&gt;pop piano music&lt;/strong&gt;, to facilitate research on various tasks related to music emotion. The dataset contains&amp;nbsp;&lt;strong&gt;1,087&lt;/strong&gt;&amp;nbsp;music clips from 387 songs and&amp;nbsp;&lt;strong&gt;clip-level&lt;/strong&gt;&amp;nbsp;emotion labels annotated by four dedicated annotators.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;For more detailed information about the dataset, please refer to our paper:&amp;nbsp;&lt;a href="https://arxiv.org/abs/2108.01374"&gt;&lt;strong&gt;EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation&lt;/strong&gt;&lt;/a&gt;.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;File Description&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;&lt;em&gt;&lt;strong&gt;midis/&lt;/strong&gt;&lt;/em&gt;:&amp;nbsp;midi clips transcribed using GiantMIDI.

	&lt;ul&gt;
		&lt;li&gt;Filename `Q1_xxxxxxx_2.mp3`: Q1 means this clip belongs to Q1 on the V-A space; xxxxxxx is the song ID on YouTube, and the `2` means this clip is the 2nd clip taken from the full song.&lt;/li&gt;
	&lt;/ul&gt;
	&lt;/li&gt;
	&lt;li&gt;&lt;em&gt;&lt;strong&gt;metadata/&lt;/strong&gt;&lt;/em&gt;:&amp;nbsp;metadata from YouTube. (Got when crawling)&lt;/li&gt;
	&lt;li&gt;
	&lt;p&gt;&lt;em&gt;&lt;strong&gt;songs_lists/&lt;/strong&gt;&lt;/em&gt;:&amp;nbsp;YouTube URLs of songs.&lt;/p&gt;
	&lt;/li&gt;
	&lt;li&gt;
	&lt;p&gt;&lt;em&gt;&lt;strong&gt;tagging_lists/&lt;/strong&gt;&lt;/em&gt;:&amp;nbsp;raw tagging result for each sample.&lt;/p&gt;
	&lt;/li&gt;
	&lt;li&gt;
	&lt;p&gt;&lt;em&gt;&lt;strong&gt;label.csv&lt;/strong&gt;&lt;/em&gt;: metadata that records filename, clip timestamps, and annotator.&lt;/p&gt;
	&lt;/li&gt;
	&lt;li&gt;
	&lt;p&gt;&lt;em&gt;&lt;strong&gt;metadata_by_song.csv&lt;/strong&gt;&lt;/em&gt;: list all the clips by the song. Can be used to create the train/val/test splits to avoid the same song appear in both train and test.&lt;/p&gt;
	&lt;/li&gt;
	&lt;li&gt;
	&lt;p&gt;&lt;em&gt;&lt;strong&gt;scripts/prepare_split.ipynb:&lt;/strong&gt;&lt;/em&gt; the script to create train/val/test splits and save them to csv files.&lt;/p&gt;
	&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Cite this dataset&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;@inproceedings{{EMOPIA},
         author = {Hung, Hsiao-Tzu and Ching, Joann and Doh, Seungheon and Kim, Nabin and Nam, Juhan and Yang, Yi-Hsuan},
         title = {{MOPIA}: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation},
         booktitle = {Proc. Int. Society for Music Information Retrieval Conf.},
         year = {2021}
}&lt;/code&gt;&lt;/pre&gt;</description>
  </descriptions>
</resource>
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